Network analysis

There are no prerequisites.

Introduction to network science. Graph theory. Real-world networks.
Node position. Spectral and distance node centrality. Clustering coefficients. Link analysis algorithms.
Link importance. Betweenness and bridgeness link centrality. Embeddedness and topological overlap.
Node similarity. Local and global node similarity. Structural and regular equivalence.
Node fragments. Egonets analysis. Network motifs and graphlets. Convex subgraphs. Node orbit distributions.
Graph partitioning. Graph bisection. Spectral analysis. Hierarchical clustering. Core-periphery structure.
Network clustering. Modularity optimization. Community detection. Blockmodeling.
Network structure. Small-world and scale-free networks. Node mixing.
Network modeling. Erdos-Renyi. Watts-Strogatz. Price, Barabasi-Albert and configuration models.
Network abstraction. Network representations. Structural network comparison. Network sampling. Network layout algorithms. Network visualization.
Network mining. Node classification and ranking. Network inference and link prediction. Machine learning with graphs.
Selected applications of network analysis. Fraud detection. Software engineering. Information science

• Barabási, A.-L., Network Science (Cambridge University Press, 2016).
• Newman, M.E.J., Networks: An Introduction (Oxford University Press, 2010, 2018).
• Coscia, M., The Atlas for the Aspiring Network Scientist (e-print arXiv:210100863v2, 2021).
• Menczer, F., Fortunato, S. & Davis, C.A., A First Course in Network Science (Cambridge University Press, 2020).
• Easley, D. & Kleinberg, J., Networks, Crowds, and Markets (Cambridge University Press, 2010).
• de Nooy, W., Mrvar, A. & Batagelj, V., Exploratory Social Network Analysis (Cambridge University Press, 2011).
• Estrada, E. & Knight, P.A., A First Course in Network Theory (Oxford University Press, 2015

The course aims at familiarizing the student with the theoretical fundamentals of network science and analysis, and the practicalities of applying network analysis to real-world problems.

After successfully completing the course, students should be able to:
• Apply the network science approach to data analysis.
• Evaluate different types of methods and models.
• Choose the correct approach for the problem at hand.
• Interpret network analysis results
• Identify potential issues.

Lectures, lab sessions, homeworks, a project and a final written exam.

Continuing (homeworks, project)
Final (written exam)
Grading: 6-10 pass, 5 fail

• Šubelj, L. & Bajec, M. Unfolding communities in large complex networks. Phys. Rev. E 83, 036103 (2011).
• Šubelj, L., Fiala, D. & Bajec, M. Network-based statistical comparison of citation topology of bibliographic databases. Sci. Rep. 4, 6496 (2014).
• Šubelj, L., Žitnik, S., Blagus, N. &Bajec, M. Node mixing and group structure of complex software networks. Advs. Complex Syst. 17, 1450022 (2014).
• Šubelj, L., Van Eck, N. J. & Waltman, L. Clustering scientific publications based on citation relations. PLoS ONE 11, e0154404 (2016).
• Marc, T. & Šubelj, L. Convexity in complex networks. Netw. Sci. 6(2), 176-203 (2018).
• Šubelj, L. Convex skeletons of complex networks. J. R. Soc. Interface 15(145), 20180422 (2018).
• Naglić, L. & Šubelj, L. War pact model of shrinking networks. PLoS ONE 14(10), e0223480 (2019).
• Šubelj, L., Waltman, L, Traag, V. & Van Eck, N. J. Intermediacy of publications. R. Soc. Open Sci. 7(1), 190207 (2020).
• Makarov, I., Kiselev, D., Nikitinsky, N. & Šubelj, L. Survey on graph embeddings and their applications to machine learning problems on graphs. PeerJ Comput. Sci. 7, e357 (2021).
• Traag, V. & Šubelj, L. Large network community detection by fast label propagation. Sci. Rep. 13, 2701 (2023).